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   "source": [
    "## Basic building blocks\n",
    "\n",
    "### Linear predictor\n",
    "\n",
    "Bambi builds **linear predictors** of the form\n",
    "\n",
    "$$\n",
    "\\pmb{\\eta} = \\mathbf{X}\\pmb{\\beta} + \\mathbf{Z}\\pmb{u}\n",
    "$$\n",
    "\n",
    "The linear predictor is the sum of two kinds of contributions\n",
    "\n",
    "* $\\mathbf{X}\\pmb{\\beta}$ is the common (fixed) effects contribution \n",
    "* $\\mathbf{Z}\\pmb{u}$ is the group-specific (random) effects contribution \n",
    "\n",
    "Both contributions obey the same rule: A dot product between a data object and a parameter object.\n",
    "\n",
    "### Design matrix\n",
    "\n",
    "The following objects are **design matrices**\n",
    "\n",
    "$$\n",
    "\\begin{array}{c}\n",
    "\\underset{n\\times p}{\\mathbf{X}}\n",
    "& \\underset{n\\times j}{\\mathbf{Z}}\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "* $\\mathbf{X}$ is the design matrix for the common (fixed) effects part\n",
    "* $\\mathbf{Z}$ is the design matrix for the group-specific (random) effects part\n",
    "\n",
    "### Parameter vector\n",
    "\n",
    "The following objects are **parameter vectors**\n",
    "\n",
    "$$\n",
    "\\begin{array}{c}\n",
    "\\underset{p\\times 1}{\\pmb{\\beta}}\n",
    "& \\underset{j\\times 1}{\\pmb{u}}\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "* $\\pmb{\\beta}$ is a vector of parameters/coefficients for the common (fixed) effects part\n",
    "* $\\pmb{u}$ is a vector of parameters/coefficients for the group-specific (random) effects part\n",
    "\n",
    "As result, the linear predictor $\\pmb{\\eta}$ is of shape $n \\times 1$. \n",
    "\n",
    "**A fundamental question:** How do we use linear predictors in modeling?\n",
    "\n",
    "Linear predictors (or a function of them) describe the functional relationship between one or more parameters of the response distribution and the predictors. "
   ]
  },
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   "attachments": {},
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   "metadata": {},
   "source": [
    "### Example: Linear regression\n",
    "\n",
    "A classical linear regression model is a special case where there is no group-specific contribution and a linear predictor is mapped to the mean parameter of the response distribution.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\pmb{\\mu} &= \\pmb{\\eta} = \\mathbf{X}\\pmb{\\beta}  \\\\\n",
    "\\pmb{\\beta} &\\sim \\text{Distribution} \\\\\n",
    "\\sigma      &\\sim \\text{Distribution} \\\\\n",
    "Y_i  &\\sim \\text{Normal}(\\eta_i, \\sigma)\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
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   "attachments": {},
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   "metadata": {},
   "source": [
    "### Link functions\n",
    "\n",
    "Link functions turn linear models in **generalized linear models**. A link function, $g$, is a function that maps a parameter of the response distribution to the linear predictor. When people talk about generalized linear models, they mean there's a link function mapping the mean of the response distribution to the linear predictor. But as we will see later, Bambi allows to use linear predictors and link functions to model any parameter of the response distribution -- these are known as distributional models or **generalized linear models for location, scale, and shape**.\n",
    "\n",
    "$$\n",
    "g(\\pmb{\\mu})  = \\pmb{\\eta} = \\mathbf{X}\\pmb{\\beta}\n",
    "$$\n",
    "\n",
    "where $g$ is the link function. It must be **differentiable, monotonic, and invertible**. For example, the logit function is useful when the mean parameter is bounded in the $(0, 1)$ domain.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "g(\\pmb{\\mu}) &= \\text{logit}(\\pmb{\\mu}) = \\log \\left(\\frac{\\pmb{\\mu}}{1 - \\pmb{\\mu}}\\right) = \\pmb{\\eta} = \\mathbf{X}\\pmb{\\beta} \\\\\n",
    "\\pmb{\\mu} = g^{-1}(\\pmb{\\eta}) &= \\text{logistic}(\\pmb{\\eta}) = \\frac{1}{1 + \\exp (-\\pmb{\\eta})} = \\frac{1}{1 + \\exp (-\\mathbf{X}\\pmb{\\beta})}\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
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   "metadata": {},
   "source": [
    "### Example: Logistic regression\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "g(\\pmb{\\mu})  &= \\mathbf{X}\\pmb{\\beta}  \\\\\n",
    "\\pmb{\\beta}   &\\sim \\text{Distribution} \\\\\n",
    "Y_i    &\\sim \\text{Bernoulli}(\\mu = g^{-1}(\\mathbf{X}\\pmb{\\beta} )_i)\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "where $g = \\text{logit}$ and $g^{-1} = \\text{logistic}$ also known as $\\text{expit}$."
   ]
  },
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   "metadata": {},
   "source": [
    "### Generalized linear models for location, scale, and shape\n",
    "\n",
    "This is an extension to generalized linear models. In a generalized linear model a linear predictor and a link function are used to explain the relationship between the mean (location) of the response distribution and the predictors. In this type of models we are able to use linear predictors and link functions to represent the relationship between **any parameter of the response distribution** and the predictors.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "g_1(\\pmb{\\theta}_1) &= \\mathbf{X}_1\\pmb{\\beta}_1 + \\mathbf{Z}_1\\pmb{u}_1 \\\\\n",
    "g_2(\\pmb{\\theta}_2) &= \\mathbf{X}_2\\pmb{\\beta}_2 + \\mathbf{Z}_2\\pmb{u}_2 \\\\\n",
    "&\\phantom{b=\\,} \\vdots \\\\\n",
    "g_k(\\pmb{\\theta}_k) &= \\mathbf{X}_k\\pmb{\\beta}_k + \\mathbf{Z}_k\\pmb{u}_k \\\\\n",
    "Y_i &\\sim \\text{Distribution}(\\theta_{1i}, \\theta_{2i}, \\dots, \\theta_{ki})\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### Example: Heteroskedastic linear regression\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "g_1(\\pmb{\\mu})     &= \\mathbf{X}_1\\pmb{\\beta}_1  \\\\\n",
    "g_2(\\pmb{\\sigma})  &= \\mathbf{X}_2\\pmb{\\beta}_2 \\\\\n",
    "\\pmb{\\beta}_1 &\\sim \\text{Distribution} \\\\\n",
    "\\pmb{\\beta}_2 &\\sim \\text{Distribution} \\\\\n",
    "Y_i  &\\sim \\text{Normal}(\\mu_i, \\sigma_i)\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Where\n",
    "\n",
    "* $g_1$ is the identity function\n",
    "* $g_2$ is a function that maps $\\mathbb{R}\\to\\mathbb{R}^+$.\n",
    "    * Usually $g_2 = \\log$\n",
    "    * $\\pmb{\\sigma} = \\exp(\\mathbf{X}_2\\pmb{\\beta}_2)$."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What can be included in a design matrix\n",
    "\n",
    "A design matrix is... a matrix. As such, it's filled up with numbers. However, it does not mean it cannot encode non-numerical variables. In a design matrix we can encode the following\n",
    "\n",
    "\n",
    "* **Numerical** predictors\n",
    "* **Interaction** effects\n",
    "* **Transformations** of numerical predictors that don't depend on model parameters\n",
    "    * Powers\n",
    "    * Centering\n",
    "    * Standardization\n",
    "    * Basis functions\n",
    "        * Bambi currently supports basis splines\n",
    "    * And anything you can imagine as well as it does not involve model parameters\n",
    "* **Categorical** predictors\n",
    "    * Categorical variables are encoded into their own design matrices\n",
    "    * The most popular approach is to create binary \"dummy\" variables. One per level of the categorical variable.\n",
    "    * But doing it haphazardly will result in non-identifiabilities quite soon.\n",
    "    * Encodings to the rescue\n",
    "        * One can apply different restrictions or contrast matrices to overcome this problem. They usually imply different interpretations of the regression coefficients.\n",
    "        * Treatment encoding: Sets one level to zero\n",
    "        * Zero-sum encoding: Sets one level to the opposite of the sum of the other levels\n",
    "        * Backward differences\n",
    "        * Orthogonal polynomials\n",
    "        * Helmert contrasts\n",
    "        * ...\n",
    "\n",
    "**These all can be expressed as a single set of columns of a design matrix that are matched with a subset of the parameter vector of the same length**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who takes care of each step?\n",
    "\n",
    "* Data matrices are built by `formulae`. \n",
    "    * Data matrices are not dependent on parameter values in any form.\n",
    "* Bambi consumes and manipulates them to create model terms, which shape the parameter vector.\n",
    "    * The parameter vector is not influenced by the **values** in the data matrix."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going back to planet Earth..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import bambi as bmb\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "az.style.use(\"arviz-darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = bmb.load_data(\"sleepstudy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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2btyYv/mbvyn5E3PgwLRx48Zcdtll+da3vlXuVrbr6enJv/zLv+Saa64pdyvPa+LEiRkzZky522AvsgcDpbS39+BNmzblH//xH1MsFgd9rq9+9av53//7f+/xP9jc0X/+53/mwx/+cDZv3lyCzhjK7MNAKe2Lr4VLqb29Pe9+97v3OMhnm6eeeirve9/7Mm/evBJ1xlBkDwZKaX/dg0sZeA87sg8DpbS/7cObNm0alLrf//738973vjfd3d2DUp+hwR4MlNJg7MG//OUv8/a3v70kQT4D4efCDAb7MFBKQ+W18IwZM/boujVr1uSv/uqv9jjIZ5vHHnssF110URYvXjygOgx99mGglPbXfdjPhhkM9mCglPa3PXgw9t4tW7bky1/+cmbPnp2tW7eWvD7PEJUEDClbt27N+9///txzzz27PD5s2LCceuqpedWrXpUpU6ZkwoQJqaurS01NTdrb27Nu3bo89thjWbBgQe6///78/ve/z5///Oe9fBdAqVRVVeW4447L9OnTM2nSpEycODFjx45NXV1damtr09nZmfXr12fx4sWZP39+br755jz22GO7rPWJT3wiY8eOzatf/eoB9TRs2LBMmTIl06dPz4tf/OJMnDgxhx9+eOrr61NXV5eurq60t7dn2bJluffee3PzzTdnxYoVu6z1jW98I4cddlj++q//ekA9DZZzzz233C2wF9mDgR3ti3vwC/nSl760V/4x5x133JHPfvazuzxWWVmZs846K695zWsyderUjBo1Kl1dXXn88cfT0tKSG2+8MQsXLuxz3c9//vP88z//c2bPnj3Y7bOPsg8DOxqK+/De9tGPfjQPPvjgLo+97GUvy9ve9raceOKJaWpqSldXV5YsWZKf/exn+d73vpfOzs6dzt+0aVM+9KEP5cc//nEOP/zwvdE++xB7MLAjezDsXfZhYEf24f4ZP358Tj311Jx22ml58YtfnFGjRmXUqFHZtGlT/vSnP+Xee+/NL37xi/z85z/f5T/O/O1vf5sPfvCD+bd/+7cUCoUy3AHlZA8GdrQv7sG//OUvc/HFF+8y9KaysjKnnnpqXvOa1+T444/PIYcckkMOOSSbNm1Ke3t7Vq5cmQcffDDz58/P//t//6/P94P7w8+FGQz2YWBH++I+PFj25N8jb9q0KR/4wAeyevXqPscKhUJe//rX55xzzsnxxx+fESNGZMOGDVm4cGFuvfXW3HjjjX3eDLlu3br8r//1v3LjjTemoaFhj++Focs+DOzIPgx7jz0Y2JE9uH8mTZqU0047LaeddlomTJiQUaNGpbGxMZ2dnXnyySczf/783H777bnrrrt2ef3NN9+c+vr6XHHFFQPuhV0T5gMMKd/73vee8zdhv/rVr87s2bMzfvz4XR5vbGxMY2NjJk6cmNNPPz1Jb3rc3XffnZ/97Gf56U9/mrVr1w5a7we6l7zkJVm0aFG522A/cPjhh+dVr3pVzjzzzEyfPj3V1dXPeW5DQ0MaGhoybty4vOpVr8pHPvKR3HDDDfnsZz+bjRs39jn/6quvzktf+tLU19f3q6dDDjkkr3zlK/PqV786p5xyyvNeX19fn/r6+owZMyazZs3KxRdfnFtvvTX/5//8n11+Dfrc5z6X17zmNRk7duxu9VLqv2udnZ2ZOXNmOjo6dhqvrKzMOeecU7J52PfZg4cuezClsi/uwbtr8eLF+drXvrbT2EknnZR77723pPOsWrUqf//3f59isdjn2DHHHJN//ud/zqRJk3Yar66uzsEHH5yjjjoqf/VXf5Xvfe97+eQnP9nnH4xcd911mTVrVmbNmlXSnhka7MNDl32YUhkq+/DFF1+cSy65ZMB1BuIHP/hBfvGLX/QZr62tzTXXXJPXve51O41XVVVl+vTpmT59ev7qr/4qF198cR544IGdzlm/fn1mz56db3/724PaO/see/DQZQ+mVOzBAzds2LBMmjQpxx13XGpra3PDDTeUuyWGCPvw0GUfplTsw/3T0NCQN77xjXnb296WY445ZpfnDB8+PPX19ZkwYULOOeecLFu2LJdeemmf18FJcuedd+ZHP/pR3vrWtw526+xj7MFDlz2YUtmX9+AHH3wwH/7wh3cZ5HPGGWfkYx/72C6/Rm3bA8eMGZOXvOQluej/Z+++w6Mo9/eP35sekhASCEjvkIjSBATEIyoePUdQBNGjglKOvXdsiKhHxApHBfWoKPrFBkqxo6IiXQQEEpr0LikkIZXM74/8CCw7G7K7s5ndzft1XVy6z+w888lDyL2T2f3MiBEqLCzU/PnzNW3aNI+b13FdGP5CDgcvchhWCaQcHjRokAYNGuT113Ki3bt36/xFo/sSAAEAAElEQVTzz3dpKlunTh2vPlj56quvmp7PJicna9KkSerevbvLcXr16qVevXpp6NChuu2227Rjxw6n5+zcuVNPPfWUnn32WY/rQfAjh4MXOQyrkMO+i4yMVLt27XT66afr8OHDmj17tmVzI3SRwcGLDIZVyGDP1KtXT5deeqmGDBmili1bmj4nKipKiYmJatOmja644gr9/vvvuv/++13OgyVp+vTpuvDCCyt+jsJaNPMBEDQOHTqkiRMnmm678847dcstt3g8Z3h4uHr27KmePXvqwQcf1Jw5c/Tee+/5WioAP7nhhht03333eb1/WFiYhg0bptNOO00jR450eYG+d+9effrppxo+fHiV5xwwYICuvPJKhYWFeV3XxRdfrFNPPVXDhg3TgQMHnLYdPnxY77zzjh555BGv5/fF119/7dLIR5LOPvtspaSk2FAR7EAGAwjEDK6qsrIyPfbYY05vgkxISNDo0aP1r3/9y9Jjvfbaa8rLy3MZP/300/XOO++c9K5NYWFhuvrqq9WoUSPdcsstOnLkiNP2J598UnPnzlVkZKSldSOwkcMAgjmHq9uhQ4dM74QcHh6uV1555aQffmjUqJGmTp2qq6++Whs3bnTatnTpUn355Zf65z//aWnNCFxkMAAy2HMOh0PNmzfXaaedptNPP12nnXaaOnTooNjYWEnSkiVLaOaDKiGHAZDDVZeSkqIbb7xRQ4YMUUxMjEf7tm7dWh9++KFuvvlmLViwwGX7iy++qIsuushvjfgReMhgAIGcwQUFBbrnnntUUFDgNO5wOPTEE0/oyiuv9Gi+mJgYXXTRRbrooouUnZ3t0b5cF4Y/kMMAAjmHrTBz5kyXDy9K5e/DjoqK8miuHTt26O2333YZr1Wrlt555x2lpqZWun9qaqqmTZumIUOGuLxve9asWbr66qvVqVMnj2pCcCOHAZDDnjv+pi5Hrw+npqZWzDdz5kya+eCkyGAAZHDVNWvWTLfeeqsuvvhij3933KVLF3322WcaNmyY0tPTXbY//fTTmj17tk+fkYY5VhRA0Jg3b57pRdOLL77YqxfmJ4qOjtbll1+u2bNn6+yzz/Z5PgDWq127tiXzdOnSRXfffbfptq+//tqjueLj4y15kdqyZUs98cQTptu+/fZb0zs5VYcZM2aYjnMHxpqFDAYQiBlcVf/3f/+nlStXOo3dfffdljel27Fjh+lFp1q1aumll1466Rs2j9e3b19df/31LuNbt27VrFmzfKoTwYccBhDMOVzdpk+frqysLJfxUaNGVfkuxrVr19YLL7ygiAjXeyFMnjzZtvNzVD8yGAAZXHW9evXS1KlTtWzZMn3zzTd64YUXNHz4cHXr1q2ikQ/gCXIYADl8comJibr//vs1b948DRs2zONGPkdFRUVp0qRJatKkicu2gwcP6vvvv/e1VAQRMhhAIGfwpEmTtGXLFpfxxx9/3ONGPieqU6dOlZ/LdWH4CzkMIJBz2FeGYWjmzJmm27x5P/Kbb76p4uJil/EHH3zwpI18jmrYsKGefvppl3HDMPTqq696XBOCGzkMgByuun/+85/64IMP9Ntvv2nOnDl65plndM0116hjx45eNwZCzUUGAyCDT65BgwZ68skn9dVXX2ngwIFeN4FPSEjQ66+/bvo77I0bN2rFihVezYvK0cwHQNCYN2+e6bi7gPVFdHS05XMCCCxXX3216Yv9lStXqrCw0IaKpPPPP19t27Z1Gd+7d6/pm1H8bfv27Vq+fLnLeL169dS3b99qrwf2IYMBWKk6M3jv3r168cUXncY6duyoq666ytLjSOUds0tLS13GR4wYoaZNm3o836233qp69eq5jJvdVQqhjRwGYKVAPBe2ypEjRzRt2jSX8aSkJN10000ezdW+fXsNHjzYZXzDhg369ddfva4RwYUMBmClUM5gqTw7e/Xq5dEHFoHKkMMArBSqOdynTx/9+9//9rqJz/Hi4uLc/oylmU/NQgYDsJKVGbx9+3bT3/9effXVfrn2WxmuC8NfyGEAVgq0c+HFixdr165dLuMdOnSocvOdo3JycvT555+7jLdt21ZXXHGFR3Odc845pjeF+fnnn/Xnn396NBeCGzkMwEqhnMNSebMEbuoCq5DBAKwUqhl86aWX6oorrjC9SamnGjRoYNpgXuK6sL/QzAdA0Fi5cqXLWGpqqlcXQAEgIiJCvXr1chk3DEMHDhywoaJy7jr97tu3r5orkWbMmCHDMFzGL730Ukte/CN4kMEArFSdGTx27Fjl5+dXPA4PD9e4ceMUFmb9r0MWLVrkMhYeHu71XSCjoqJMu21v3rxZq1ev9mpOBCdyGICVAvVc2AqLFi0y/RquvPJKxcXFeTzfiBEjTMfN7riM0EQGA7BSKGcw4A/kMAArkcNVc8EFF6hWrVou49yBsWYhgwFYycoMfuONN1RSUuI0VqdOHd17770+1egNrgvDX8hhAFYKtHPhTz/91HTc7AYrJ/P111+rqKjIZfy6667z6j1hZteFDcPQnDlzPJ4LwYscBmClUM5hwGpkMAArkcFVc8kll5iOc13YP/gUNoCgcOTIEWVlZbmMN2nSxIZqQkdJSYl++uknzZ8/X+vWrdOuXbuUn5+vsLAwJScnq3Xr1urZs6f69++vhg0b+nSsvLw8bdmyxWX89NNP92ne4+f/+eeftXTpUmVkZGjXrl3Kzc1VcXGxoqOjVa9ePbVo0UJdunTR+eefr/bt21tyXHeKior066+/atmyZRVrm5mZqcLCQkVGRio+Pl716tVTmzZt1L59e/Xs2VOnnXZapRcRPv/8c+3evbvicW5urstzfvzxR+3du7dKNQ4bNqzG3yX3lFNOMR3/66+/bDvxd1fTwYMHq7WOsrIy0ztXSPafMKB6kcH+QQb7DxkcHKojg7/88kv9+OOPTmPXXnut0tLSLJn/eIcPH9aaNWtcxlNTU9WgQQOv5z3nnHM0ZcoUl/Evv/xSHTt29HpeBA9y2D/IYf8hh4NDIJ4LW+Gbb74xHb/00ku9mq9ly5bq1KmTVq1a5TQ+b948lZaW0uQ2xJHB/kEG+w8ZHBxCNYMBq5HD/kEO+w85HBzI4ZOLjo5Whw4dtGzZMqfxgwcP6siRIwoPD7epMlQXMtg/yGD/IYODgxUZnJmZadpk/dprr1V8fLxP9XmK68LwF3LYP8hh/yGHg0OgnAvn5uZq3rx5LuPR0dEaMGCAx/OZXReOiorSP/7xD6/q6927t1JSUlw+2Pn111/rzjvv9GpOBBdy2D/IYf8hh4NDqOYwYCUy2D/IYP8hg4MDGXxyDRs2VKNGjZy+9yRxIxw/Cfp3m5eWlmrDhg1at26dNm3apJycHOXm5io/P19Hjhyx5BgOh0PvvvuuJXMB8E5WVpbKyspcxqOiomyoxtl5552nXbt2OY0988wzGjRokNdzzpw5Uw899JDTWOPGjfXDDz94PeeJPv30U/33v/91+0Juz5492rNnjxYsWKCXXnpJl1xyie69916lpKR4dby1a9fq2muvdRlfv369V/MdtWPHDr3++uuaM2eOCgsLTZ9z+PBhbd++Xdu3b9fPP/+siRMnqlOnTrrzzjt11lln+XR8s3refvttff755zp8+LDpc44cOaLCwkL99ddfysjI0Ny5cyWV3y2of//+GjJkiFJTU132mzFjhpYuXVrp8b/99lt9++23Var1kksuqfEvziMjI03HY2Njq7mSY9zVFBMTU611/PLLL6Y/H7p06aLWrVtXay2wFxlMBrtDBjsjgz3j7wzOycnR008/7TTWsGFD3X777ZbMf6Lt27e73AlSkjp16uTTvB06dFBkZKTL3AsWLPBpXgQPcpgcdoccdkYOeyYQz4WtsHDhQpexFi1aqFWrVl7P2bdvX5dmPvn5+Vq5cqW6devm9bwIfGQwGewOGeyMDPZMqGYwYDVymBx2hxx2Rg57hhyumnr16rmMlZWVKSsry3QbQgsZTAa7QwY7I4M9Y0UGf/XVVyoqKnIaczgcuvzyy32qzRtcF4a/kMPksDvksDNy2DOBci7s7vv3ggsuUO3atT2aq7i4WL/99pvLePfu3b1u8hcWFqZzzjlHn376qdP4n3/+qb1797r9IChCBzlMDrtDDjsjhz0TijkMWI0MJoPdIYOdkcGeIYOrpl69ei7NfP766y+bqgltQdvMJz09XR9//LG+/PJLHTp0yG/HMQxDDofDb/MDqBp3d/jat29fNVcS/AoLC3XvvfeadvVz58iRI/rss8/0008/6cUXX1SvXr38WGHVa5oyZYqmTJmi4uJij/dftWqVRo4cqYEDB2rs2LE+vxgrLS3V5MmT9cYbb3hVjyRlZ2fr/fff1/vvv+/zSQuqxl23SF/ulOQrdzX52u3WUzNmzDAdHzx4cLXWAfuRwdYhg8uRwZD8n8ETJkxw+UXSo48+qri4OEvmP5HZXQEk3/M7OjpaSUlJ2r9/v9P4xo0blZmZqeTkZJ/mR+Ajh61DDpcjhyEF5rmwr/bu3audO3e6jJ955pk+zetu/2XLltHMJ8SRwdYhg8uRwZBCM4MBfyCHrUMOlyOHIZHDVWUYhul4dHR0NVcCO5DB1iGDy5HBkKzJ4K+//tpl7PTTT7clx7kuDH8hh61DDpcjhyEFzrmwu/cje9OYb/Xq1aYfhrTiuvCJzXwkaenSpbrkkkt8mhuBjxy2DjlcjhyGFJo5DFiNDLYOGVyODIZEBleV2XVhrgn7R5jdBXgqLy9PY8aM0eDBg/Xhhx8qJydHhmH45Q+AwJGYmKiwMNcfWatXr/ZrQ69QU1JSohtvvNGjF+bHy8zM1PXXX+/1/lbJy8vTv//9b02aNMnrF8JHff755xo6dKhycnK8niM7O1vXXXedXnnlFZ/rCSSjR49W+/btXf7MnDnT7tIss2TJEpexVq1aKSkpyYZqyi1evNhlrFatWqbdV/0lKyvLtLNvrVq19M9//rPa6kBgIIOtQQa7IoPdI4N9n/vEX3ydf/756tevn89zu+PuTZtWdDVPTEw0HV+7dq3PcyPwkcPWIIddkcPukcPBac2aNabjHTp08GneU0891fRNA+np6T7Ni8BHBluDDHZFBrtHBgM4ihy2Bjnsihx2jxzGUTt27HAZi4mJqfF38KwpyGBrkMGuyGD3yOCTO3z4sH7//XeX8Z49e/pcmze4Lgx/IYetQQ67IofdI4erx4YNG0yv5TZu3NirPHeXi75eFz7ttNNMx7kuXDOQw9Ygh12Rw+6Rw9XD6hwGrEYGW4MMdkUGu0cGV49gyGCz68L16tWzoZLQF2F3AZ44cOCAhg4dqu3bt1c023E4HDZXBaA6hIWFqWnTptq2bZvTeElJiV599VU99NBDNlUWXJ599lmXRiHh4eE644wz1LJlSyUnJys7O1s7duzQkiVLVFJS4jJHSUmJ7r33Xr3//vs6/fTTq6v0Cvn5+Ro5cqRWrVrl9jlNmjRR586dVbduXcXHxysnJ0c7d+7U0qVLdfjwYZfnr1mzRjfffLOmTZvmtqurO9nZ2Ro2bJg2bNjg9jlRUVHq2rWrGjVqpLp16yo8PFw5OTnav3+/1qxZQ8dYm3z77bfau3evy7idd1D4448/TN+AcvHFF3v8vemL2bNnm/77v+iiixQXF1dtdSAwkMHWIIPJYBzjzwwuKirSmDFjnBr01qpVS4899pjPcweajIwMnX322XaXAT8jh61BDpPDOCYQz4Wt4O57sXXr1j7NGxsbq0aNGrlctOIOKaGPDLYGGUwG45hQzWDAH8hha5DD5DCOIYerJicnx/R8t0mTJjZUAzuQwdYgg8lgHGNFBv/++++m/07s+LdhF64L1wzksDXIYXIYxwTKufCnn35qOj5o0CCvPgvm7nuxTZs2Hs91vObNmysyMtLl5wLXhWsGctga5DA5jGNCNYcBq5HB1iCDyWAcQwZXzYYNG5Sdne0y3rRp0+ovpgYImmY+eXl5GjlyZEUwm32zHv9hPQChp2fPni4vziVp6tSpqlOnjm644YZqbXYRbFavXq0///yz4nFYWJiGDx+ukSNHKiUlxeX5OTk5mj59ul555RWXF+mFhYV66KGH9NlnnykyMtLvtR9lGIYeeOAB0xfmUVFRGjRokEaMGKEWLVqY7l9cXKyZM2dq0qRJOnjwoNO23377TZMnT9Ztt91W5XpKS0t1++23u31h3rJlS91xxx3q27evatWq5XaePXv26JtvvtEXX3yh1atXu33etGnTnB6fd9552rVrl9PYM888o0GDBlX5a6ipduzYoSeffNJlPCkpSVdddZUNFZXfuemhhx5yeT0THR2tUaNGVWst7rqpDh48uFrrQOAgg31DBpPBOMbfGfzqq69q69atTmN33HGHGjZs6PPclaldu7bpeG5urs9zu+sIb9YJG6GJHPYNOUwO45jqOBfeuXOnpk6dqt9++02bN29WVlaWcnNzFRcXp8TERNWpU0dt27ZV9+7d1a1bN8s+EOguF5s3b+7z3M2bN3eZf9euXSorKzO9MxBCBxnsGzKYDMYxoZzBgL+Qw74hh8lhHEMOV92XX36p0tJSl3GaB9QsZLBvyGAyGMdYlcHr1q0zHW/ZsqXT4yNHjuiXX37RDz/8oD/++EM7d+7U4cOHFR8fr6SkJKWkpKhr167q06ePOnfu7PW/K64Lw5/IYd+Qw+QwjgmU90iXlJRo9uzZLuNhYWFe/z2a5WJMTIwaNGjg1XxHhYeHq3Hjxi7vO9u5c6dP8yJ4kMO+IYfJYRwTyjkM+AMZ7BsymAzGMWRw1ZnVJ3Fd2F+CppnPlClTtHHjRpcmPkc/8B4TE6O2bduqdevWql27tuLj43kjPRBi/vGPf+ijjz4y3fbyyy/ryy+/1LXXXquLLrpICQkJ1Vxd4Nu8eXPF/8fGxmrKlCnq2bOn2+cnJibqpptuUt++fTV8+HBlZWU5bd+4caPefPNN3XLLLX6r+UQffvih5s2b5zLetGlTTZw4UR06dKh0/6ioKP3rX//SeeedpxtvvNHlYv/kyZN13nnn6dRTT61SPa+//rqWLl1quu22227TzTffrIiIk0dtw4YNNXz4cA0fPlzLly/X5MmTq3R8eOeHH37Q448/rv379zuNOxwOPfXUU6pTp06117RixQo98sgjTifQR913330ub0DxpzVr1igjI8NlvEWLFurWrVu11YHAQgb7hgwmg1HO3xmckZGht956y2ns1FNP1bXXXuvTvFXhrvY9e/b4NG9xcbHLz4CjeLNIzUEO+4YcJodRrrrOhT///HN9/vnnLuPZ2dnKzs7Wtm3btGrVqoq7TnTv3l3XX3+9zjnnHJ+Ou3v3bpexiIgI1atXz6d5Jal+/fouYyUlJdq/f79OOeUUn+dH4CKDfUMGk8EoF+oZDPgLOewbcpgcRjlyuOpKSkr0zjvvmG4799xzq7ka2IkM9g0ZTAajnJUZfPy/q+M1atSo4v/nzp2rl156yfT66dE83rJli5YuXaopU6aoQYMGuvnmm3X55Zd7/MEkrgvDn8hh35DD5DDKBdJ7pH/44QfTfOvVq5dTlnvC7LpwSkqK6c3iPVW/fn2XZj67d++WYRiWzI/ARg77hhwmh1Eu1HMY8Acy2DdkMBmMcmRw1WVnZ7v9uct1Yf8IimY++/fv13vvvWfayKddu3a66aabdN555ykmJsamCgFUh169eqlr165asWKF6fYNGzbo0Ucf1RNPPKFu3brpjDPOUOfOndWxY0clJiZWc7WBKywsTC+//HKlL8yPl5qaqjfeeEPDhg1TYWGh07YpU6boX//6l5KTk/1RqpPs7Gy98MILLuONGjXS9OnTTbuFulO/fn29++67GjhwoFOnytLSUr355pt66aWXTjrHrl27NGXKFNNtY8eO9bpbY7du3Vw+hA7vFRQUKDc3V1u2bNHq1av1xRdfKD093eV54eHheuKJJ9SvXz+/11RUVKS8vDxt27ZNa9eu1VdffaUVK1ZUNCg83q233lotDQiON2PGDNPxwYMHV2sdCCxksDXIYDK4JqnuDC4rK9Njjz3mdNfgsLAwPfHEE9XSjd/dL9XMOsR7Yu3atS5d7486sXM8Qhc5bA1ymByuSQLxXNidZcuWadmyZerQoYNefPFFt3dPOZnMzEyXseTkZEveVOmuIVBmZibNfEIcGWwNMpgMrklqYgYD/kIOW4McJodrEnLYN++++67pnW/btGmjHj162FAR7EIGW4MMJoNrEn9n8I4dO1zGoqOjFRcXp7y8PI0ePVrfffedR3Pu27dPY8eO1dSpU/Xaa6+pdevWVd6X68LwJ3LYGuQwOVyTBPq5sD/ej2yWi1bc4EWS6b+zkpISHTp0iJ+zNQA5bA1ymByuSWpiDgP+QAZbgwwmg2sSMtg3EydO1KFDh1zGzz77bDVp0sSGikJfUDTz+fHHH1VcXFzxxvujnY2HDx+u+++/v1o+mAcgMIwbN05XXnml8vPz3T6npKREixYt0qJFiyrGmjdvrtNPP11dunRRt27d1L59+xrbIX3w4MHq27evR/t07NhR//73v/XKK684jRcVFWnWrFkaMWKEhRWae+edd5Sbm+s0Fh4ertdee82jF+ZH1a5dWxMmTNA111zjNP7tt99q7969J/0w1uuvv67i4mKX8SuuuMLrF+bw3ltvvaUJEyZ4tW+LFi30n//8R2eccYalNX399de68847vdq3fv36evzxx6v9ZKGoqEhffPGFy3h4eLgGDhxYrbUg8JDBviODy5HBoSVQMvi9997T6tWrncauuuoqdezY0ee5qyI5OVlt2rTRpk2bnMbXr1+v/fv3q379+l7N+/PPP7vdlp2d7dWcCE7ksO/I4XLkcGgJlBy2wtq1azVo0CA99dRT+uc//+nx/mZ3sbDqTjzu5iGLawYy2HdkcDkyOLSQwUD1IId9Rw6XI4dDCzlsvc2bN2vSpEmm2+68884a+zO0JiODfUcGlyODQ4tdGfzXX3+5jMXHxysvL0+jRo3SypUrvapJkrZu3aorr7xSkyZNUu/evau0D9eF4W/ksO/I4XLkcGgJxnPhffv2acGCBS7jderU0QUXXODVnMXFxTp8+LDLuFXXhePj403Hs7Oz+aB4DUEO+44cLkcOhxZyGPA/Mth3ZHA5Mji0kMHWW7x4saZPn+4yHhYW5vXnoHFyYXYXUBXHX6Q42sjnn//8p0aPHk0jH6CGadu2rV5++WXFxsZ6tN+2bds0d+5cPfnkk7r00kvVs2dP3X333frpp5905MgRP1UbeGJiYnTXXXd5te+///1v0xfBn3zyiY9VnVxJSYnpcQYPHqy0tDSv5+3WrZvOPPNMp7HS0lLNmjWr0v3y8vI0Z84cl/H69evrwQcf9LoeVJ+IiAidddZZev755/Xll18GxBs2HQ6HunTposcff1zff/+9LXeD/O6775STk+My/re//c3rN5sgdJDBviGDnZHBNZc/MnjXrl2aOHGi01hKSoruvvtun+f2RPfu3V3GSktL9fHHH3s1n7t/f0fl5eV5NS+CEznsG3LYGTlcc1XHuXBSUpJ69OihIUOGaOTIkbrttts0fPhwDRw4UGlpaQoLc39JIj8/X3fffbdX2Wn2ps1atWp5PI+ZuLg40/HK3jyA0EEG+4YMdkYG11yhnMGAP5HDviGHnZHDNRc5XLnCwkLdddddKioqctnWp08f/f3vf7ehKtiNDPYNGeyMDK65rMpgs0bukZGReuCBB0wb+aSkpKh///4aOXKkbrrpJg0ZMkQdOnRwO39ubq5uueUWbdiwoco1cV0Y/kQO+4YcdkYO11yB8B7pzz//3PTnT//+/RUVFeXVnGbXhCWuC8M65LBvyGFn5HDNFao5DPgTGewbMtgZGVxzkcGVO3jwoO677z4ZhuGybciQITr99NNtqKpmiLC7gKrYsmWLU0e88PBwjR492saKANjpb3/7mz7++GPdeeed+vPPP72aIzs7W19++aW+/PJLpaSk6MYbb9RVV12liIig+LHotfPOO0/16tXzat/Y2FgNGDBAb7/9ttP45s2btWnTJrVp08aKEk0tXLhQBw8edBm/7rrrfJ77sssu05IlS5zGli9frhtvvNHtPvPnzze9IHDVVVe57cofjMaPH6/x48fbXYZfxMXFqW7duoqJiQmYrruxsbGqX7++ZReVvDFjxgzT8csvv7yaK0GgIoO9Rwa7IoPdI4M9M3bsWJfvi4cfftiyuy5VVf/+/U27VL/99tsaOHCgmjRp4tF8kydP1oEDB9xuN+v6jtBGDnuPHHZFDrtHDnuuR48eOv/889W3b1+1aNGi0uceveD59ttva/v27abPGTt2rJo3b+5yQbUyZrkYGRlZ5f0r4+5nJFlcc5DB3iODXZHB7pHBnguEDAb8jRz2Hjnsihx2jxz2XKjk8COPPGLavCAhIUFPP/10tdaCwEIGe48MdkUGu0cGn1xhYaHL2N69e7V3716nsbZt2+rBBx9Unz59TI+3a9cuvfTSS6YfyCkoKNAdd9yhGTNmuP0Q//G4Lgx/I4e9Rw67IofdI4f9a+bMmabjvrwf2V0mWnVd2N08ZHHNQg57jxx2RQ67Rw77lz9yGPA3Mth7ZLArMtg9Mti/AjWDS0pKdOedd5r+Hrpx48Z64IEHbKiq5nB/658AkpmZKUkyDEMOh0NdunQx7fYGoOZo166dZs+erbFjx6pBgwY+zXXgwAE99dRT6t+/v1atWmVRhYHpn//8p0/79+/f33T8jz/+8Gnek/nll19cxlq3bm3JCUG3bt1cxlatWmXaYbCyesLDw21/UYWqy8nJ0ezZs3Xbbbfpoosu0rfffmt3STp8+LC++eYbPfjggzrvvPP00UcfVevxd+/ercWLF7uM161bV3379q3WWhDYyGDvkMGuyOCayeoMnjt3rn7++WensbPPPtvnf3Pe6Natm7p06eIynp+fr3vuucejOyb+/PPPev311yt9Tmlpqcc1IviRw94hh12RwzWT1Tl8ySWX6Msvv9S0adM0fPjwk354UZLi4+N11VVX6auvvtKoUaNML5gdOXJEDz74oAoKCqpci1kuWnUR3t08JSUllsyP4EAGe4cMdkUG10yhnMFAdSCHvUMOuyKHayZy2L1XX31Vc+fONd329NNP65RTTqm2WhCYyGDvkMGuyOCayaoMrsrvYS+44ALNnDlTZ599ttsPaTRu3FjPP/+8nnnmGYWHh7ts37Jli954440q1cR1YVQHctg75LArcrhmsvs90suXL9fWrVtdxk899VSlpaV5Pa+7TPT3dWGyuOYhh71DDrsih2umUM1hoDqQwd4hg12RwTUTGeze448/rmXLlrmMR0ZG6sUXXwyphlWBKChayp14YaN9+/Y2VQIgkERGRuqqq67SkCFD9Msvv2j27NluOyBWxZYtWzRs2DA9/fTTGjBggMXVBoauXbv6tH9aWppiY2Nd3kC2evVqXXbZZT7NXZnff//dZczsgrg3mjZtqoiICKdftOfk5Gjnzp1q2rSp6T6rV692GWvTpo3q169vSU3w3BlnnKE777zTacwwDOXl5SknJ0cbNmxQRkaG6Rs8tm3bpttvv11XXHGFxo4da/qGDW+0bdvWtKb8/Hzl5uZq8+bNWrdunekbMg8cOKAxY8boxx9/1EsvvaTY2FhLaqrMzJkzVVZW5jJ+6aWXhnwXYniODPYcGeyKDA4NdmZwdna2/vOf/ziNxcTE6PHHH/f8C7HIbbfdplGjRrmMr1q1Stdee62ef/55tWrVyu3+hmHo448/1pNPPnnSN4JY9ZoFwYcc9hw57IocDg12nwsPGTLE69ojIiL0wAMPqHXr1nr44Yddtu/Zs0fvvPOObrnllirNFxYWpiNHjjiNmZ3jesPdPGFhQXGvBFiIDPYcGeyKDA4NZDBQ/chhz5HDrsjh0EAOW+Ozzz7TpEmTTLeNHDlSF154od9rQHAggz1HBrsig0ODXRl8sjsod+3aVRMnTqzynIMGDdKhQ4f0zDPPuGybPn26rr/++ip9cIHrwqgO5LDnyGFX5HBosPtc2FMzZswwHR88eLBP87q7Puvv68Inez2C0EQOe44cdkUOhwZyGKheZLDnyGBXZHBoIIOt8corr7it7aGHHlLnzp2rt6AaKCg+mR0bG+vU0KdOnTr2FQMg4EREROjcc8/Vueeeq9LSUq1du1ZLly7VypUrtXbtWu3Zs6fKcxUVFemBBx5Q7dq1dc455/ix6uqXkpKiunXr+jRHWFiY2rZt6/LiNCMjw6d5K1NWVqaNGze6jFd2sdtTiYmJOnjwoNNYZmam6YvzkpIS0w6JHTt2tKweeK5z584nfeFYVFSkr776Su+++67WrVvnsv3jjz9WQUGBnnvuOUsuvLRu3fqkb7QsLS3V/Pnz9e6772rp0qUu23/88UfdeOON+t///qeoqCifa3LHMAzNnDnTdBsdZFEZMrhqyGD3yODgZ2cGjx8/3uX755ZbbnH7C9bq0KdPH40cOVJvv/22y7a1a9fqkksu0YABA/T3v/9daWlpSk5OVlFRkfbt26eFCxfqs88+c1mjxo0ba9euXS7z+fO1AYIDOVw15LB75HDwC8RzYU8NHjxYW7duNb3j8bRp03T99dcrMjLypPNERES4NPM58bG33M1TlboQmsjgqiGD3SODgx8ZDNiHHK4actg9cjj4kcO++/777/Xoo4+abhswYIAeeOABvx0bwYsMrhoy2D0yOPjZlcFmv/s9KjIyUuPHj/f4gxjXXXedvv32W/32229O4zk5Ofr88881dOjQk87BdWFUJ3K4ashh98jh4BdM58L5+fn6+uuvXcajo6N9/vC0uxuTcl0Y/kQOVw057B45HPzIYcAeZHDVkMHukcHBjwz23QcffKD//ve/pttuvPFGXXPNNdVcUc0UFM18TjnlFG3atKnicW5uro3VAAhkERER6tSpkzp16lQxlpmZqXXr1mn58uVasmSJVq5cWWkH9rKyMt1///2aPXu2TjnllOoou1o0b97csnlOfHGenZ1tydxmDh48qKKiIpfxCRMmaMKECX47bk5Ojun4/v37Tb9/2rRp47daYI3o6GgNHDhQAwYM0FtvvaWXXnrJ5e9yzpw56tKlS7W9EI2IiFC/fv3Ur18/zZgxQ+PGjVNhYaHTc5YsWaKXXnpJDz74oN/qWLx4sembQTp37qzWrVv77bgILWSwe2SwZ8jg0OOPDD76BsfjtWnTRiNHjrSsbm/de++9Sk9P16JFi1y2lZSUaObMmW6b6J0oNjZWEyZMMF2XuLg4n2tF6CCH3SOHPUMOh55APBc+0a233qpZs2Zp3759TuOZmZlatGiR/va3v510jpiYGJd/MyeeX3vL3TzR0dGWzI/gRga7RwZ7hgwOPTUlgwE7kcPukcOeIYdDDzns3uLFi3X33Xc73Yn0qHPPPVfjx4+3pcERggsZ7B4Z7BkyOPT4I4PNfvd71IUXXujVvzuHw6Hrr7/epZmPJP36669VauYjcV0Y9iCH3SOHPUMOh55AORf+8ssvdfjwYZfxfv36KTEx0ae5Y2JiTMetui5cUFBgOs51YRxFDrtHDnuGHA49NSGHATuRwe6RwZ4hg0MPGeze7Nmz9dRTT5lu+9e//qV77rmnmiuqucLsLqAq0tLSZBhGxZsF9u/fb3NFAIJJcnKy+vTpo7vuukvTp0/X/Pnzdffdd6tOnTpu98nJydHkyZOrr8hqEB8f77d5/Nlk7a+//vLb3JU5dOiQ6XhWVpbpeO3atf1ZDiwUHh6uG264QePGjTPd/txzzykzM7Oaqyq/A+OkSZNM7+AwdepU046zVvn000/d1gT4ggwuRwZ7hgwOXVZlcGFhoR5//HGnMYfDoSeeeCIg7oQUERGhN954Q5dddplP88THx2vy5Mlq166d6faUlBSf5kfoI4fLkcOeIYdDV6CeC0vlb7ocPny46TazD0GYMftezMvL86Wsk87Dm1zgDhlcjgz2DBkcukI9g4FAQw6XI4c9Qw6HLnLY2cqVK3XzzTebvjn6zDPP1MSJExURERT3BUQAIoPLkcGeIYNDl5UZXNnvYX25HnvOOecoOTnZZfy3336TYRhVmoPrwggU5HA5ctgz5HDosvtceMaMGabjVrwfOS4uTuHh4S7jVl0Xzs/PNx2v7OcpQA6XI4c9Qw6HrlDOYSDQkMHlyGDPkMGhiwx2Nm/ePD300EOmTaoGDBigsWPHVn9RNVhQNPM555xzKv7fMAwtW7bMxmoABLsGDRropptu0rx583TxxRe7fd7MmTNte+OWP1j14jwhIcFlzN0LWSu463Lvb8XFxabj7rr38+I8+AwZMsT0Z0BBQYE+/PBDGyoqf80zYsQIl/GysjJNnTrVL8fMzc3VvHnzXMZr1aqlf/7zn345JmouMtg3ZDAZHCp8zeD//ve/2r59u9PY4MGD1a1bN8tq9FVUVJTGjx+vp556Sg0aNPB4/06dOumTTz5Rr1693Ha1r1+/vo9VoqYhh31DDpPDoSIQz4Ul52sAx1uzZk2V9je7CG/VnWHczcObNlFVZLBvyGAyOFSEagYDgY4c9g05TA6HCnJYSk9P1/XXX296R8jOnTtr8uTJio6Otvy4qLnIYN+QwWRwqLAig939HjYsLEydO3f2urawsDCnu7kflZOT43ItujJcF0YgIod9Qw6Tw6HCjnPhP//8U7///rvLeOPGjdWrVy+f53c4HKaN/qy6LuzuA7zc5AWeIId9Qw6Tw6EiFHMYCHRksG/IYDI4VJDB0oIFC3T33XertLTUZVu/fv00fvx4ORyOaq+rJguKZj7nn3++kpKSKh4fPHhQy5cvt7EiAKEgISFBL774ogYNGmS6vbi4WAsXLqzmqnAis+5/gFXuvvtu0/Hvvvuumis55qabblJsbKzL+Lx586p85ydPzJkzx/Sk88ILL7TspB44ERkcHMhg+JO3Gbxp0yaXBnfJycm6//77rSrNUkOGDNF3332nRx99VN27d1dkZKTb54aFhalbt2566aWX9NFHH6lVq1aSpAMHDpg+v2XLln6pGaGPHA4O5DD8KRDPhVu3bm36oYwdO3ZUaX+zDzPk5ua6vcjqCXdZ7M0HM1CzkcHBgQyGP4ViBgPBghwODuQw/Kkm5/DmzZs1cuRI0zddp6am6o033lBcXJylxwSOIoODAxkMf/I1g0855RTT8UaNGvn8vqbU1FTTcW8+0MV1YQQicjg4kMPwp+o+F54xY4bp+GWXXaawMGs+umZ2Xfivv/6yZG6zeerUqaOYmBhL5kfNQg4HB3IY/hSKOQwEAzI4OJDB8KeanMHLli3TbbfdZtq46qyzztJLL72kiIiIaq0JUlCseExMjG644QY9++yzcjgcMgxDL7zwgqZPn253aQBCwOOPP64FCxZo//79LtuWLFmi/v3721CV9fLy8iyZJzc312XMn10mo6KiTMf79eunDh06+O24p556qum4u1/Im60LAl/Tpk2VmpqqjIwMp/GMjAzl5eXZ0swmLi5OvXv31vfff+80np2drc2bN6tNmzaWHs/dCcPgwYMtPQ5ghgz2DBlMBocSbzN469atLh2i27Rpo//7v//zuAZ33zvr16/Xa6+95jLesmVL/eMf//D4ONHR0Ro2bJiGDRum/Px8rVmzRgcPHlRmZqYOHz6suLg4NWnSRKeffrqSk5Nd9nf3wY127dp5XAtwPHLYM+QwORxKAvFcWJLq1q3rctfEqt5FsUmTJqbju3btUuvWrX2qa9euXS5jderUoQEuvEYGe4YMJoNDSShmMBBsyGHPkMPkcCipqTm8bds2DR8+3LQpQatWrfTOO+8oMTHRsuMB7pDBniGDyeBQ4msGu/vdrxX5ZdZUT/I+i7kujEBFDnuGHCaHQ0l1ngsfOXJEs2bNchl3OBxuP0jtjSZNmrh8PQcPHlRBQYHpjVQ9YXZduHHjxj7NCZDDniGHyeFQEoo5DAQTMtgzZDAZHEpqagavWrVKN954owoKCly2devWTa+++qrbf3vwr6Bo5iNJ1113nb7++mutWrVKkrRy5UpNnDhRd955p82VAQh2MTExuvzyy00/sGvXnUePHDli+ZxWvTg3mychIcGSuc24u/DerVs3jRgxwm/HdScpKcl0PCcnp5orgVVOPfVUlxfnZWVl2rdvn21v2jz11FNdmvlI0u7duy1t5rNhwwatWbPGZbxFixbq3r27ZccB3CGDfZ+HDCaDg5lVGbx06VItXbrUsrrWrVundevWuYyff/75XjXzOV5cXJzOPPNMj/ZZv3696Xjnzp19qgUgh32fhxwmh4NZIJ4Lm138LSoqqtK+LVq0MB3fsmWLz818tm3b5jLGnZDhCzLY93nIYDI4mIVaBgPBhhz2fR5ymBwOZjUth3ft2qXhw4ebvlG9adOmmjp1qmkjAcAfyGDf5yGDyeBg5ksGt2rVynQ8Li7O57rczWHFh3S4LoxAQg77Pg85TA4Hs+o6F/7pp5904MABl/FevXpZ2hDH7LqwYRjatm2bUlNTvZ43NzfXtBEu14XhK3LY93nIYXI4mIVaDgPBhAz2fR4ymAwOZjUtg9etW6d///vfys/Pd9l2+umn6/XXX/e5AS68F2Z3AVUVFhamV155RY0aNZLD4ZBhGJoyZYomTpyosrIyu8sDEOR69OhhOp6VlXXSfR0Oh8uYrz+X/HHHU7MPHlk1j7u71FihYcOGpmts111hGzRooPDwcJfxzZs321ANrFC3bl3TcTtPuKqrpk8//dR0nO7bqE5ksG/zkMFkcDALxAwORCtXrnQZS0lJ4c0isAQ57Ns85DA5HMwCMYcPHTrkMubujicncnfHlBMvxnlq586dpnWlpaX5NC9ABvs2DxlMBgezUMtgIBiRw77NQw6Tw8GsJuXwvn37NHz4cO3evdtlW8OGDfXuu++qQYMGPh8H8AQZ7Ns8ZDAZHMx8yeDTTjvNdNzsQwmecjeHPz+oVBmuC8OfyGHf5iGHyeFgVl3nwjNmzDAdHzx4sKXH8dd1YbMbv0lcF4Y1yGHf5iGHyeFgFmo5DAQbMti3echgMjiY1aQM3rhxo0aOHGl63bl9+/Z66623bLuxDcoFTTMfqfyixPTp0yvupHu0oc9VV12lX375xebqAASz+vXrm44XFBScdF+zO7QcPnzYp3rMOrv76sCBAz7PW1ZWpo0bN7qMt2/f3qd5KxMVFaWmTZu6jP/5559+O2ZlIiIiTC+Qr1692oZqYAV3dzaMiIio5kqOcVeT2Ymht0pKSjR79mzTYwwcONCy4wAnQwZXDRlMBoeiQMzgQHPo0CHT7/Hzzz/fhmoQisjhqiGHyeFQFIg5fPDgQZcxd3dgOVFaWpqioqJcxs0+/OAJd/t37NjRp3kBMrhqyGAyOBSFWgYDwYgcrhpymBwORTUlhw8ePKjhw4dr+/btLttSUlI0depU7sYMW5DBVUMGk8GhyJcMbtu2rWrVquUybsWHLtx9OMjdnbj9ievC8DdyuGrIYXI4FFXHufDBgwf1008/uYwnJibqggsusOw4kvvrtL///rtP83JdGP5EDlcNOUwOh6JQy2Eg2JDBVUMGk8GhqKZk8JYtWzRixAjTJmWtWrXSO++8w3vAAkDQNPNZtmyZli1bpu3bt+v+++9XmzZtJJU39Fm1apVuuOEG9evXTw8//LCmT5+ub7/9VosWLarYz9c/AEJbSUmJ6XhV7nhm1pXO14vFf/zxh0/7u/Pbb7/5tH96errpCcvpp5/u07wn06lTJ5exZcuWyTAMvx7XHbNfzG/cuFEHDhyo1jrMOpDatSbBzOwNkpK9H1ZwV5OVXW1/+OEH0xfqZ599NndhRLUig6uGDC5HBoeWQMzgQPPVV1+ptLTUZbx///42VINQRA5XDTlcjhwOLYGWw5s3bzb94ERVP1wYHR2tzp07u4z/9ttvKi4u9rquX3/91XS8d+/eXs8JSGRwVZHB5cjg0BJqGQwEI3K4asjhcuRwaKkJOZydna3hw4ebvtk5KSlJ77zzjlq0aOH1/IAvyOCqIYPLkcGhxZcMjoiI0Jlnnukyvnv3buXl5flUV0ZGhum4u7s2+xPXheFv5HDVkMPlyOHQUh3nwrNmzTL9OdO/f39FR0dbdhxJatq0qel588KFC32a1+y6cGxsrOk1aMBT5HDVkMPlyOHQEmo5DAQbMrhqyOByZHBoqQkZvGPHDg0fPtz0e7RZs2aaOnWqLb/rhqugaeYzbNgwXXvttbr22mt10003afPmzZKO/WAyDEM7d+7UZ599pnHjxunOO+/UyJEjK/bx5c91111n55cOoBrs27fPdPyUU0456b5mAX70Z5Q3Dh8+7LcX519++aVP+8+dO9d03N9d58855xyXsaysLNuarZ199tkuY0eOHNGnn35arXWY3XWosLCwWmsIBWYnrZGRkWrUqJEN1ZRbvny56Xjz5s0tO8aMGTNMxwcPHmzZMYCqIIOrhgwuRwaHFm8yuF+/flq/fr0lf77//nvTY1x22WWmz3/ttdcs+9qrwjAMffDBBy7jbdu2Vffu3au1FoQucrhqyOFy5HBoCbRzYbO7U0hShw4dqjyH2b+Zw4cPu537ZEpKSkxfL7Rv354muPAZGVw1ZHA5Mji0hGIGA8GGHK4acrgcORxaQj2H8/LyNGrUKG3YsMFlW+3atfX222+rbdu2Xs0NWIEMrhoyuBwZHFp8zeBzzz3XZaysrEy///671zWVlZVp1apVLuMpKSmmdwX3J64LozqQw1VDDpcjh0NLdZwLz5w503TcX+9HNvs3s337dq1bt86r+f766y/T92336tVLUVFRXs0JHI8crhpyuBw5HFpCMYeBYEIGVw0ZXI4MDi2hnsH79u3T8OHDtXfvXpdtjRo10tSpU3mfcwAJmmY+RxmGUfHn6GOHw1Hx5/jtVv4BENp++eUX0/E2bdqcdF+zNzutXr3a61o+++wzv73A+vHHH/XXX395tW9hYaHmzJnjMt6yZUu/v+Hr3HPPVVxcnMv4lClT/Hpcd8455xzTeqZPn678/Pxqq8OsBl+7vNY0K1euNH3R2qFDB0VGRtpQUfmL6ZUrV7qMp6SkWHY35H379mnBggUu48nJyaZvgAH8iQw+OTL4GDI4dARiBgea2bNna/369S7jI0aMsKEahCpy+OTI4WPI4dARaDlcVFSkqVOnmm7r2bNnlee5+OKLTe9K8uGHH3pV11dffWX6vcWdkGEFMvjkyOBjyODQEaoZDAQbcvjkyOFjyOHQEeo5fPjwYV1//fVas2aNy7a4uDi9+eabOvXUUz2eF7ASGXxyZPAxZHDosCKDL7roItMP0n/22Wde1zV//nxlZma6jJ955plez+ktrgujOpDDJ0cOH0MOh47qOBdetWqVNm7c6DKelpbmt6bx7q7Xentd+JNPPtGRI0dcxgcMGODVfMCJyOGTI4ePIYdDR6jmMBBMyOCTI4OPIYNDR6hn8MGDB3Xddddp586dLttSUlI0depUyz6DDGsEXTOf4xv3HHV8w53jt1vxB0DgMGs8YYXMzEzTF51S1d4kZRauu3btMr1zy8nk5eXprbfe8ni/qiooKNDLL7/s1b7/+9//dODAAZfxIUOG+FjVycXHx2vQoEEu47/++qvP3UO9ERcXp0svvdRlfN++fXr22WerrY6UlBSXsS1btlTb8YOdYRhu/77OO++8aq7mmAkTJqisrMxl3MqaPv/8c9MLT5deeikNFGCKDPYdGWwNMjg0BGoGB5Jdu3bpP//5j8t4u3btNHDgwOovCLYih31HDluDHA4NgZjDr732muldcBITE9WnT58qz9OwYUPTu6MsWLDA4zs0l5aW6vXXX3cZj4yMJItrEDLYd2SwNcjg0BDKGQz4AznsO3LYGuRwaAj1HC4uLtYtt9yiFStWuGyLjY3V66+/rs6dO3tbKmoYMth3ZLA1yODQYFUGJyYm6uKLL3YZ//bbb7Vt2zav6nrzzTdNt1X3TdC4LozjkcO+I4etQQ6Hhuo6F54xY4bp+ODBgy07xom6du1q+gHszz77zPTDjJXJzc3Ve++95zJet25dnX/++V7XiOBDDvuOHLYGORwaQjmHAauRwb4jg61BBoeGUM/gnJwcjRgxwvR7Ijk5WVOnTlXz5s39WgM8F3TNfI5H4x2gZrn99ts1ZMgQ/fzzz5bNWVpaqkceeUTZ2dku2+rWravevXufdI6uXbsqPDzcZfy1117zuJ7HH39cu3bt8ng/T8yYMUPz58/3aJ/Vq1ebXsCOiorSZZddZlFllbvxxhtVq1Ytl/GHHnpIK1eutOw4W7du1aFDh6pUT3R0tMv4Rx99pE8++cSyeirTvn17l7GFCxequLjYkvlHjx6t9u3bu/yZOXOmJfN7asOGDZbON2HCBNM3NEZFRVX5zRAbN26saChohffff19z58413XbFFVdYdhx3f4f80g7ukMHWIIMrRwYfQwbXbPv27dPw4cNdfj46HA499thjpj/3ENrIYWuQw5Ujh48hh09u165dlp0Lf/75527vpHLVVVeZ3m25Mv/+979Nx8eMGaOioqIqz/PGG29o06ZNLuMDBw5U/fr1PaoJwYsMtgYZXDky+Bgy+OQCOYMBq5HD1iCHK0cOH0MOn1yg5nBpaanuuOMOLVq0yGVbVFSUXn31VXXv3t3rWlHzkMHWIIMrRwYfQwZX3fXXX6+wMOe3vJeUlGj06NGmNzSrzLvvvmtaV9OmTXXRRRd5NJcvuC6ME5HD1iCHK0cOH0MO+66wsFBffPGF6TEGDBhgyTHMOBwOjRo1ymW8uLhYjz/+uEfn7+PHj1dmZqbL+LBhw0z/DSB0kcPWIIcrRw4fQw77zq4cBqxGBluDDK4cGXwMGew7uzI4Ly9Po0aN0vr16122JSYm6p133jFtfAv7BVUzH8Mwqv0PgMCyevVqXX/99br88sv10UcfKTc31+u5Dhw4oJtvvlk//PCD6fYRI0YoIiLipPOkpKTorLPOchmfP3++3n///SrVUlJSogcffNBtEw8rlZWV6a677tKSJUuq9PyMjAzdeOONKiwsdNl24403Kjk52eoSTaWkpOi+++5zGS8sLNSwYcM0ffp0n+Zfvny5br31Vv3jH/8w7Sh6olNOOUW33Xab6bZHH31Ur732msdvFDhah9kFBjOdOnVyGfvrr7/05JNPWvYCPZDcf//9GjZsWJW/d93Jzs7W6NGj9fbbb5tuHzp0qBo0aFCluV566SVddtllmjdvnsrKyryuqaCgQBMmTNCTTz5puv3CCy/Uaaed5vX8x1u+fLm2bt3qMt6pUye1bdvWkmMgNJHBviODzZHBgS8QMziQzZw506uu+5K0bNkyDR06VNu3b3fZNnLkSPXo0cPX8hCkyGHfkcPmyOHAF4g5PHXqVA0YMEBz5sxRaWmpV/UcOXJEL7zwgkaPHm26PSUlxW1jnsqceeaZ6tu3r8v4hg0bdO+996qkpOSkc8ydO1evvPKKy3itWrV0++23e1wTghsZ7Dsy2BwZHPjIYMB+5LDvyGFz5HDgI4erpqysTA888IB+/PFHl22RkZGaNGmS6c8s4GTIYN+RwebI4MAXiBl8VOvWrfWvf/3LZXzFihW66667qvz38dlnn2nChAmm22688cYq/Uw6iuvC8Ady2HfksDlyOPAFcg678/XXXysvL89lvF+/fqpTp44lx3Dn0ksvNf2Q64IFC/T0009X6bNgb775pj799FOX8VNOOUXXXXedJXUiuJDDviOHzZHDgY8cBuxFBvuODDZHBgc+MrhqCgsLdfPNN+uPP/5w2RYfH6///e9/Sk1N9cux4buqX3WwWUZGht0lAAggf/zxh/744w89/fTT6tevn84//3z17NlTdevWPem+mzZt0pw5czRt2jTl5+ebPqddu3Ye/RL2yiuvNO0A+tRTT2n79u26/fbblZCQ4LK9tLRU8+fP14svvqjNmzdXjLdp08b07t++aN26tf78808ZhqGCggINHz5cw4cP16hRo1SvXj2X5x86dEjTp0/XK6+8YvpCr1WrVrrhhhssrfFkrrnmGi1btkxfffWV03hxcbHGjh2rjz76SKNGjdJ5552nuLi4Suc6fPiw1q5dq++//17ffvutVx1OR40apYULF5re5W7ixImaO3eubr/9dvXt21exsbFu59mzZ4+++eYbzZ071/QFlTu9evVSSkqKy8nExx9/rG+++UY9evRQ06ZNFRcX53JnIqn8rgFm35eBbOnSpbr22mvVpEkT/eMf/1C/fv2UlpZWpbsfbN68WbNnz9ZHH32krKws0+e0adNGd955p0c1paen69Zbb1VKSoouvPBC/f3vf9dpp5120u9BSdq9e7fmzJmjDz/8ULt37zZ9TkpKisaMGeNRTZWZMWOG6fjgwYMtOwZCGxnsOTLYGRlMBpvxJoMD1S+//KKHHnpIp512mi666CL169dPLVu2dPv8srIyrVixQh999JFmz55t+pw+ffronnvu8VfJCCLksOfIYWfkMDlsxpsc3rhxo+677z49/fTT+vvf/64LL7xQnTt3Pun3YF5enr744gu9/fbbpo1mJSksLEz/+c9/vP57evTRR7Vs2TKXn3Xfffedrr76ao0dO1YdOnRw2S8nJ0evvvqq3nvvPdM3d951110h0XgQ3iGDPUcGOyODyWAzoZbBP/zww0mv6e/cudN0fNmyZSe9g13t2rU1dOhQr2pDcCOHPUcOOyOHyWEzoZDDr776quldHyWpY8eOSk9PV3p6epXnq8x5553HG0BrIDLYc2SwMzKYDDbjy3Xhe+65Rz/99JPL99K3336rQYMG6cEHH1SfPn3kcDhc9t21a5defvllzZkzx/T3v3/72988fu8U14XhT+Sw58hhZ+QwOWzG6vdn2fl+5PDwcI0bN07XXHONS9PdadOmaevWrXrsscfUvHlzl3337dun5557TnPmzDGd+7HHHlOtWrX8UjeCAznsOXLYGTlMDpsJpRyWpM8//9ztZ6COcvf76R9//FF79+6tdN9GjRpp4MCB3paHIEUGe44MdkYGk8FmQiGDx44dq6VLl5pu6969uxYsWKAFCxZYcqxLL71UjRs3tmQulHMYVWk5DAABoEuXLjp8+HClz2nYsKHatm2rhg0bqk6dOoqKilJhYaHy8/O1c+dOrV+/Xvv27at0juTkZH344Yemv7ytzKhRo9wGXnR0tHr06KGWLVsqISFBhw4d0p49e7R06VIdOnTI6bk9e/bUgAED9MgjjziNN27c2G1X0BONHj1an332mdPYZZddpvj4eE2bNs1pPDw8XN26dVOLFi1Ut25d5eTkaMeOHVq8eLHbbo3R0dGaNm2aaadHd5YsWaJrr73WZXz9+vVVnkMqfyF+8803V/riIiIiQmlpaWrTpo0SExMVHx+vgoIC5ebmKjs7Wxs3btS2bdtUVlZmuv+XX36p1q1bV6menJwcDRs2rNKvIzo6Wl27dlXjxo2VnJyssLAwHTp0SPv27dPatWtNfwlR1XV566233N4t6GS+//57NWnSxO12s+8jSXrmmWc0aNAgr47pi0svvdT0gwARERFq2bKlWrdurcTERCUkJCg2NlaHDx9WXl6edu3apfT0dB08eLDS+Rs2bKgPPvjAoxebt9xyi77//nuXcYfDoebNm6tt27aqU6eOEhISFBcXp4KCAuXn52vv3r1KT08/6S+gateuralTp5p+yNAb+fn56tOnj8vP0tjYWC1YsEDx8fGWHAehhwwmgyUy+ERksL0ZbIWdO3fq/PPPdxm/7LLLNH78eK/nvfvuu/Xll186jSUnJ+vUU09VkyZNlJiYKIfDoczMTB04cECrV6+udI3OOOMMvfnmm1VqFIjQRA6TwxI5fCJy2N4cfvrpp/Xee++5jDscDjVr1kzt2rVTUlKS4uPjVatWLeXn5ysnJ0fr169XRkbGSe9MMmbMGF1zzTVVrsfMV199pbvvvtvtHRc7dOigzp07KykpSQUFBdq0aZMWL16soqIi0+dfcMEFeuWVV3yqCcGHDCaDJTL4RGQwGVwZd3+HVvHk5xKCHzlMDkvk8InIYXL4RP7O3uPZ9b2A6kcGk8ESGXwiMjiwrguvWbNGQ4cOVUFBgen2lJQUnXnmmapfv76io6N18OBBrVu3TmvXrnX7++KmTZtqxowZSkxM9KgWrgvDauQwOSyRwycihwMrh4+3Y8cOXXDBBS752qhRI33//femHyr1h7ffflvPPvus6TaHw6EuXbrotNNOU2JiovLy8pSenq7ly5e7NAA66rrrrtPDDz/sz5IRoMhhclgih09EDpPDlRk2bJjbpgJW6NGjh8vPFIQmMpgMlsjgE5HBZPCJ/J27x3vvvfd05plnVsuxaooIuwsAACvt2bNHe/bs8Xr/Fi1aaPLkyR6/MJekcePGaciQIaYvAIqKivTLL7/ol19+qXSOtLQ0vfLKK/ruu+88Pn5VPPjgg9q4caMWL15cMXbkyBEtWbJES5YsqdIckZGRev755z16YW6lqKgoTZ48WWPGjHH7xrTS0tKKbqz+lpiYqHfffVe33HKLVqxYYfqcoqIi026cVhgxYoSWLl2q+fPn+2X+YFBaWqqNGzdq48aNXs/RuXNnTZw4UaeccoolNRmGoa1bt7q9s2JVtGrVShMnTlS7du0sqUkqP/E0+yXHhRdeSCMf+IwMrhwZbD0y2H6BmMGBKjMz06tO1xdeeKEmTJigmJgYP1SFUEIOV44cth45bL9AzGHDMLRt2zZt27bNq/0TEhI0duxY9e/f3+da/vGPfygzM1NPPvmk6Qc01q5dq7Vr11Zprl69eumFF17wuSaEJjK4cmSw9chg+5HBQOAghytHDluPHLYfOQwEBjK4cmSw9chg+wVSBp922mmaMmWKbr75ZtP3QB04cEBz586t8nzt2rXT5MmTPW7k4w7XheFv5HDlyGHrkcP2C6QcPt6MGTNMr8Nedtll1dbIR5JGjhypgwcP6n//+5/LNsMwtGLFCrffuye65JJLNHr0aKtLRAghhytHDluPHLYfOQwEBjK4cmSw9chg+5HBCCV8ZwAIGtdcc42aNm3ql7mjoqJ0yy23aPbs2WrVqpVXczRu3Fjvv/++1+Hep08fvf/++0pISPBq/6qIjIzU66+/rn79+nm1f506dTRlyhT9/e9/t7gyz0RFRWn8+PF64YUXlJKSYuncXbp0UZ06dTzaJykpSe+9955uuOEGRUZG+lyDw+Go8nPDwsL02muv6fbbb+euPF6Ii4vTY489punTpwdME4HIyEjdeuutmjVrlqWNfKTyEwYzgwcPtvQ4CD1ksO/I4JMjg2uWQMzgQJKYmKgnn3xSkyZN4g2bIIctQA6fHDlcswRiDvfo0UOzZs2y9MOL11xzjf773//69PPlyiuv1BtvvKHo6GjL6kLwIIN9RwafHBlcs9SUDAasQA77jhw+OXK4ZiGHgaohg31HBp8cGVyz+CODe/bsqY8++kgtW7b0aZ4LL7xQH374YaV3qvY3rgvjeOSw78jhkyOHaxZ/nQuXlZXp888/dxl3OBwaNGiQZcepqvvvv1+PP/64oqKivNo/PDxct912myZMmMCHL2swcth35PDJkcM1S03JYcBXZLDvyOCTI4NrFjIYgSbC7gIAoKruu+8+3XfffcrIyNC8efO0ZMkS/fHHHyooKPB6zqZNm2rAgAEaNGiQJS/8W7Vqpc8++0wTJ07UJ598oiNHjpx0n5SUFN1222268sorPXpR5q2YmBi9+uqr+vTTTzVp0iTt27fvpPuEh4erf//+uvfee9WgQQO/11hV/fv317nnnquPPvpI//d//6cdO3Z4PIfD4VBaWprOOeccDRw4UC1atPCqlsjISN1777267LLL9MYbb+iLL75QcXGxR3PUq1dPl1xyia688kqP9jt6EWHUqFH67rvvtGLFCmVkZGjPnj3Kz8/X4cOHq/S9GOheeeUVzZ8/Xz/99JOWL1/u07/91NRUDRo0SJdccomSkpK8nmfs2LE699xz9dNPP2nx4sXKzc31eq7mzZvrsssu02WXXeaXN5D++eef+v33313GmzVrpu7du1t+PIQWMtgaZLAzMjh4BGIGB7ILLrhA+/bt08qVKz3++69bt66GDBmikSNHWnb3RwQ/ctga5LAzcjh4BGIO9+/fX6WlpVqyZIk2b97s9TyRkZH6+9//rmuuuUZnnHGG1/NU5oILLlDnzp318ssva9asWSopKanSfh07dtQ999yjXr16+aUuBAcy2BpksDMyOHiQwYC9yGFrkMPOyOHgQQ4D9iGDrUEGOyODg0cgZrA77dq10+zZs/Xuu+/q3Xff1YEDB6q8b48ePXTnnXeqW7duPtXAdWFYjRy2BjnsjBwOHsGUw7/++qv27NnjMn7mmWfa1iTv6quv1llnnaXnn39e8+bNU1lZWZX26927t+6//36deuqpfq4QgY4ctgY57IwcDh7kMGAfMtgaZLAzMjh4kMGoCRyGYRh2FwEA3iopKdHatWu1du1abd26Vdu2bdPOnTuVm5ur/Px8FRYWKjo6WnFxcYqPj1fdunXVrl07paWlqUOHDurQoYPfatu5c6d++OEHLViwQNu2bVNmZqYOHz6s2NhYNWzYUB06dNA555yj888/3+tO8L4qLi7WTz/9pPnz5ys9PV27du1SXl6ewsLClJSUpDZt2qhnz566+OKL1bhxY5+OtXjxYl133XVOY2FhYUpPT/dp3uOtXbtWCxcu1B9//KFt27Zp7969FS9MY2NjFRcXpzp16qhFixZq1aqVUlNT1aNHDyUnJ1tWw1F5eXn68ccftWzZMm3YsEE7d+7UoUOHVFJSopiYGMXFxal+/fpq3bq12rdvr969eystLa1aTtBCQWlpqTZs2KDVq1dr48aN2rlzp3bu3KmsrCwVFBSosLBQMTExSkhIqPi3n5aWplNPPVUdO3b0uqNuZQzD0ObNm7V69WqtX79eO3bs0M6dO5WZman8/HwVFRUpKipK8fHxSkhIUFJSktq3b69TTz1Vp59+ulJTUy2vCfAnMtg3ZDAZHKwCMYMD1aFDh/T7779r5cqV2rBhg3bs2KEDBw7o8OHDKi4uVmxsrOrUqaPmzZsrLS1NvXv31plnnmlJ53aEPnLYN+QwORysAjGHs7KylJ6ervT0dG3evFl79uzRnj17lJ2drcLCQhUVFSkmJkaJiYlKSEhQ/fr11bFjR3Xu3FmdO3eu1g8p/PXXX/ruu++0ePFibdy4Ufv27VNBQYEiIyOVmJioVq1aqXPnzjrvvPPUsWPHaqsLwYUM9g0ZTAYHKzIYCAzksG/IYXI4WJHDgP3IYN+QwWRwsArEDHanuLhYCxYs0IIFC7Ru3Tpt375dubm5MgxDSUlJSkpKUrNmzdSrVy/16dNHzZs3t/T4XBeGP5HDviGHyeFgFUw5HIh27typ7777TkuXLtXmzZt14MABFRUVKTo6WklJSWrVqpW6deumfv36qU2bNnaXiwBGDvuGHCaHgxU5DNiPDPYNGUwGBysyGKGKZj4AgGrx008/6YYbbnAaS0hI0PLly22qCACAmoEMBgDAPuQwAAD2IIMBALAPOQw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      "text/plain": [
       "<Figure size 4800x2250 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_data(data):\n",
    "    fig, axes = plt.subplots(2, 9, figsize=(16, 7.5), sharey=True, sharex=True, dpi=300, constrained_layout=False)\n",
    "    fig.subplots_adjust(left=0.075, right=0.975, bottom=0.075, top=0.925, wspace=0.03)\n",
    "\n",
    "    axes_flat = axes.ravel()\n",
    "\n",
    "    for i, subject in enumerate(data[\"Subject\"].unique()):\n",
    "        ax = axes_flat[i]\n",
    "        idx = data.index[data[\"Subject\"] == subject].tolist()\n",
    "        days = data.loc[idx, \"Days\"].to_numpy()\n",
    "        reaction = data.loc[idx, \"Reaction\"].to_numpy()\n",
    "\n",
    "        # Plot observed data points\n",
    "        ax.scatter(days, reaction, color=\"C0\", ec=\"black\", alpha=0.7)\n",
    "\n",
    "        # Add a title\n",
    "        ax.set_title(f\"Subject: {subject}\", fontsize=14)\n",
    "\n",
    "    ax.xaxis.set_ticks([0, 2, 4, 6, 8])\n",
    "    fig.text(0.5, 0.02, \"Days\", fontsize=14)\n",
    "    fig.text(0.03, 0.5, \"Reaction time (ms)\", rotation=90, fontsize=14, va=\"center\")\n",
    "\n",
    "    return axes\n",
    "\n",
    "plot_data(data);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The model**\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mu_i & = \\beta_0 + \\beta_1 \\text{Days}_i + u_{0i} + u_{1i}\\text{Days}_i \\\\\n",
    "\\beta_0 & \\sim \\text{Normal} \\\\\n",
    "\\beta_1 & \\sim \\text{Normal} \\\\\n",
    "u_{0i} & \\sim \\text{Normal}(0, \\sigma_{u_0}) \\\\\n",
    "u_{1i} & \\sim \\text{Normal}(0, \\sigma_{u_1}) \\\\\n",
    "\\sigma_{u_0} & \\sim \\text{HalfNormal} \\\\\n",
    "\\sigma_{u_1} & \\sim \\text{HalfNormal} \\\\\n",
    "\\sigma  & \\sim \\text{HalfStudentT} \\\\\n",
    "\\text{Reaction}_i & \\sim \\text{Normal}(\\mu_i, \\sigma) \n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Written in a slightly different way (and omitting some priors)...\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mu_i & = \\text{Intercept}_i + \\text{Slope}_i \\text{Days}_i \\\\\n",
    "\\text{Intercept}_i & = \\beta_0 + u_{0i} \\\\\n",
    "\\text{Slope}_i & = \\beta_1 + u_{1i} \\\\\n",
    "\\sigma & \\sim \\text{HalfStudentT} \\\\\n",
    "\\text{Reaction}_i & \\sim \\text{Normal}(\\mu_i, \\sigma) \\\\\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "We can see both the intercept and the slope are made of a \"common\" component and a \"subject-specific\" deflection. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the general representation written above...\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\pmb{\\mu} &= \\mathbf{X}\\pmb{\\beta} + \\mathbf{Z}\\pmb{u} \\\\\n",
    "\\pmb{\\beta}    &\\sim \\text{Normal} \\\\\n",
    "\\pmb{u}        &\\sim \\text{Normal}(0, \\text{diag}(\\sigma_{\\pmb{u}})) \\\\\n",
    "\\sigma         &\\sim \\text{HalfStudenT} \\\\\n",
    "\\sigma_{\\pmb{u}} &\\sim \\text{HalfNormal} \\\\\n",
    "Y_i  &\\sim \\text{Normal}(\\mu_i, \\sigma)\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Formula: Reaction ~ 1 + Days + (1 + Days | Subject)\n",
       "        Family: gaussian\n",
       "          Link: mu = identity\n",
       "  Observations: 180\n",
       "        Priors: \n",
       "    target = mu\n",
       "        Common-level effects\n",
       "            Intercept ~ Normal(mu: 298.5079, sigma: 261.0092)\n",
       "            Days ~ Normal(mu: 0.0, sigma: 48.8915)\n",
       "        \n",
       "        Group-level effects\n",
       "            1|Subject ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 261.0092))\n",
       "            Days|Subject ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 48.8915))\n",
       "        \n",
       "        Auxiliary parameters\n",
       "            Reaction_sigma ~ HalfStudentT(nu: 4.0, sigma: 56.1721)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = bmb.Model(\"Reaction ~ 1 + Days + (1 + Days | Subject)\", data, categorical=\"Subject\")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7ffa7cd9d450>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.build()\n",
    "model.graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrices\n",
       "\n",
       "                  (rows, cols)\n",
       "Response:               (180,)\n",
       "Common:               (180, 2)\n",
       "Group-specific:      (180, 36)\n",
       "\n",
       "Use .reponse, .common, or .group to access the different members."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm = model.response_component.design\n",
    "dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResponseMatrix  \n",
      "  name: Reaction\n",
      "  kind: numeric\n",
      "  shape: (180,)\n",
      "\n",
      "To access the actual design matrix do 'np.array(this_obj)' \n",
      "\n",
      "[249.56   258.7047 250.8006 321.4398 356.8519]\n"
     ]
    }
   ],
   "source": [
    "print(dm.response, \"\\n\")\n",
    "print(np.array(dm.response)[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CommonEffectsMatrix with shape (180, 2)\n",
      "Terms:  \n",
      "  Intercept  \n",
      "    kind: intercept\n",
      "    column: 0\n",
      "  Days  \n",
      "    kind: numeric\n",
      "    column: 1\n",
      "\n",
      "To access the actual design matrix do 'np.array(this_obj)' \n",
      "\n",
      "[[1 0]\n",
      " [1 1]\n",
      " [1 2]\n",
      " [1 3]\n",
      " [1 4]]\n"
     ]
    }
   ],
   "source": [
    "print(dm.common, \"\\n\")\n",
    "print(np.array(dm.common)[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroupEffectsMatrix with shape (180, 36)\n",
      "Terms:  \n",
      "  1|Subject  \n",
      "    kind: intercept\n",
      "    groups: ['308', '309', '310', '330', '331', '332', '333', '334', '335', '337', '349', '350',\n",
      "      '351', '352', '369', '370', '371', '372']\n",
      "    columns: 0:18\n",
      "  Days|Subject  \n",
      "    kind: numeric\n",
      "    groups: ['308', '309', '310', '330', '331', '332', '333', '334', '335', '337', '349', '350',\n",
      "      '351', '352', '369', '370', '371', '372']\n",
      "    columns: 18:36\n",
      "\n",
      "To access the actual design matrix do 'np.array(this_obj)' \n",
      "\n",
      "[[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(dm.group, \"\\n\")\n",
    "print(np.array(dm.group)[:14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CommonTerm(  \n",
       "  name: Intercept,\n",
       "  prior: Normal(mu: 298.5079, sigma: 261.0092),\n",
       "  shape: (180,),\n",
       "  categorical: False\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.response_component.intercept_term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Days': CommonTerm(  \n",
       "   name: Days,\n",
       "   prior: Normal(mu: 0.0, sigma: 48.8915),\n",
       "   shape: (180,),\n",
       "   categorical: False\n",
       " )}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.response_component.common_terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1|Subject': GroupSpecificTerm(  \n",
       "   name: 1|Subject,\n",
       "   prior: Normal(mu: 0.0, sigma: HalfNormal(sigma: 261.0092)),\n",
       "   shape: (180, 18),\n",
       "   categorical: False,\n",
       "   groups: ['308', '309', '310', '330', '331', '332', '333', '334', '335', '337', '349', '350', '351', '352', '369', '370', '371', '372']\n",
       " ),\n",
       " 'Days|Subject': GroupSpecificTerm(  \n",
       "   name: Days|Subject,\n",
       "   prior: Normal(mu: 0.0, sigma: HalfNormal(sigma: 48.8915)),\n",
       "   shape: (180, 18),\n",
       "   categorical: False,\n",
       "   groups: ['308', '309', '310', '330', '331', '332', '333', '334', '335', '337', '349', '350', '351', '352', '369', '370', '371', '372']\n",
       " )}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.response_component.group_specific_terms"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terms not only exist in the Bambi world. There are three (!!) types of terms being created.\n",
    "\n",
    "* Formulae has its terms\n",
    "    * Agnostic information design matrix information\n",
    "* Bambi has its terms\n",
    "    * Contains both the information given by formulae and metadata relevant to Bambi (priors)\n",
    "* The backend has its terms\n",
    "    * Accept a Bambi term and knows how to \"compile\" itself to that backend.\n",
    "    * E.g. the PyMC backend terms know how to write one or more PyMC distributions out of a Bambi term."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Could we have multiple backends?** In principle yes. But there's one aspect which is convoluted, dims and coords, and the solution we found (not the best) prevented us from separating all stuff and making the front-end completely independent of the backend."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formulae terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Intercept': Intercept(), 'Days': Term([Variable(Days)])}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.common.terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1|Subject': GroupSpecificTerm(\n",
       "   expr= Intercept(),\n",
       "   factor= Term([Variable(Subject)])\n",
       " ),\n",
       " 'Days|Subject': GroupSpecificTerm(\n",
       "   expr= Term([Variable(Days)]),\n",
       "   factor= Term([Variable(Subject)])\n",
       " )}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.group.terms"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bambi terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Intercept': CommonTerm(  \n",
       "   name: Intercept,\n",
       "   prior: Normal(mu: 298.5079, sigma: 261.0092),\n",
       "   shape: (180,),\n",
       "   categorical: False\n",
       " ),\n",
       " 'Days': CommonTerm(  \n",
       "   name: Days,\n",
       "   prior: Normal(mu: 0.0, sigma: 48.8915),\n",
       "   shape: (180,),\n",
       "   categorical: False\n",
       " ),\n",
       " '1|Subject': GroupSpecificTerm(  \n",
       "   name: 1|Subject,\n",
       "   prior: Normal(mu: 0.0, sigma: HalfNormal(sigma: 261.0092)),\n",
       "   shape: (180, 18),\n",
       "   categorical: False,\n",
       "   groups: ['308', '309', '310', '330', '331', '332', '333', '334', '335', '337', '349', '350', '351', '352', '369', '370', '371', '372']\n",
       " ),\n",
       " 'Days|Subject': GroupSpecificTerm(  \n",
       "   name: Days|Subject,\n",
       "   prior: Normal(mu: 0.0, sigma: HalfNormal(sigma: 48.8915)),\n",
       "   shape: (180, 18),\n",
       "   categorical: False,\n",
       "   groups: ['308', '309', '310', '330', '331', '332', '333', '334', '335', '337', '349', '350', '351', '352', '369', '370', '371', '372']\n",
       " ),\n",
       " 'Reaction': ResponseTerm(  \n",
       "   name: Reaction,\n",
       "   prior: Normal(mu: 0.0, sigma: 1.0),\n",
       "   shape: (180,),\n",
       "   categorical: False\n",
       " )}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.response_component.terms"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random idea:** Perhaps in a future we can make Bambi more extensible by using generics-based API and some type of register. I haven't thought about it at all yet."
   ]
  }
 ],
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